Survey of Middlewares for Internet of Things

Rathod, Dilip; Chowdhary, Girish V.

doi:10.1109/icrtac.2018.8679249

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…The Internet of Things is a hot research area primarily for two reasons: fist, recent studies [7] points that trillions of dollars will be in the IoT market in the next years; and second because it presents big challenges, such as interoperability, complexity and security [8].…”

Section: Background a Internet Of Thingsmentioning

confidence: 99%

The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

Barros

Neto

et al. 2022

IEEE Access

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Due to the large variety of Internet of Things (IoT) platforms, selecting the right one to implement an IoT solution is a tough task. To mitigate right selection by the developer, this paper presents a Systematic Multivocal Mapping Study on IoT platforms and its main software elements, to define their anatomy considering how they were studied by the market analysts and academia. By using a precise protocol defined on this work, it was possible to select 63 academic articles and industry reports that perform IoT platform descriptions, evaluations and comparisons. As results, this paper identified the most important IoT platforms are AWS IoT, Azure IoT, Watson IoT, PTC ThingWorx and Google IoT. Its main capabilities are Interoperability, Security & Privacy, Developer Support, Data Management, Device Management and Services Management. It was also defined an architectural model with the main platform components highlighted according to their relevance, the main communication models (Publish/Subscribe and REST APIs) and the common API that should be implemented by the IoT platforms.

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Section: Background a Internet Of Thingsmentioning

confidence: 99%

The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

Barros

Neto

et al. 2022

IEEE Access

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“…Scalability of IoT middleware is mostly neglected area by research community [3,4,26]. The scalability testing of IoT system can be done using quantitative measurement and quantitative analysis.…”

Section: Literature Surveymentioning

confidence: 99%

“…For steady state the arrival rate and service time µ should follow /µ <1. The expected number of requests in at any time for M/M/c/K system is given below [3]:…”

Section: Micro Data Centermentioning

confidence: 99%

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Scalability of M/M/c Queue based Cloud-Fog Distributed Internet of Things Middleware

Rathod¹,

Chowdhary²

2019

IJANA

Self Cite

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The Internet of Things (IoT) extends the Internet wherein real world things are part of a computing network. The IoT has seen exponential growth and according to Cisco prediction about 50 billion devices will be connected by 2020. Handling this massive scale is challenging research issue. In this paper, we use cloud-fog-edge based IoT middleware for distributed IoT service provisioning. We model IoT middleware using queueing network, perform analytical analysis of IoT middleware components. Followed by dynamic scaling algorithm which considers contention and coherency as limiting factors for scalability. It is used for quantitative analysis of IoT middleware with the increasing workload. The scalability function was evaluated using the simulation for important performance and scalability parameters namely throughput, CPU utilization and response time. It is observed that because of contention and coherency overhead, the proposed approach is able to scale sub-linearly which is practical compared ideal scalability of multi-server queueing network and not very restrictive as given universal scalability law(USL) applied for tightly coupled systems.

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“…For the smooth growth of IoT based systems/application, the researchers/designers must address these issues. The middleware is the well-known answer to these requirements of Internet of Things [2]. Middlewares has been used in many fields of distributed computing, and in a variety of ways since they were introduced e.g.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing of Fog Computing Centers: Minimizing Response Time of High Priority Requests

Rathod¹,

Chowdhary²

2019

IJITEE

Self Cite

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 Abstract: Fog computing is one of the enabling computing technology which primarily aims to fulfill the requirements of the Internet of Things (IoT). IoT is fast-growing networking and computing sector. The scalability of users, devices, and application is crucial for the success of IoT systems. The loadbalancing is an approach to distribute the load among computing nodes so that the computing nodes are not overloaded. In this paper, we propose the priority-based request servicing at fog computing centers. We particularly address the situation when the fog node in fog computing center (FCC) receives more workload than their capacity to handle it. The increased workload is shifted to nearby fog nodes rather than to the remote cloud. The proposed approach is able to minimize the offloading the high priority request to other nodes by 11% which proves the novelty of our proposed.

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Survey of Middlewares for Internet of Things

Cited by 8 publications

References 12 publications

The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

Scalability of M/M/c Queue based Cloud-Fog Distributed Internet of Things Middleware

Load Balancing of Fog Computing Centers: Minimizing Response Time of High Priority Requests

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